Representations of words in a vector space have been studied exhaustively in the NLP literature. Beginning with a one-hot vector (the very first representation of words), other word representation methods such as latent semantic analysis [1]} and latent Dirichlet allocation [2]} have been proposed to improve NLP task performance over time. Various methods that represent words as dense vectors (referred to as “word embedding”), including GloVe [3]}, word2vec (SG and continuous bag of words) [4]}, are considered as the state of the art in this field. In this paper, we focus on SG, but the proposed approach can be applied to any other word embedding model.
